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Introduction to Accounting Data Analytics and Visualization に戻る

イリノイ大学アーバナ・シャンペーン校(University of Illinois at Urbana-Champaign) による Introduction to Accounting Data Analytics and Visualization の受講者のレビューおよびフィードバック



Accounting has always been about analytical thinking. From the earliest days of the profession, Luca Pacioli emphasized the importance of math and order for analyzing business transactions. The skillset that accountants have needed to perform math and to keep order has evolved from pencil and paper, to typewriters and calculators, then to spreadsheets and accounting software. A new skillset that is becoming more important for nearly every aspect of business is that of big data analytics: analyzing large amounts of data to find actionable insights. This course is designed to help accounting students develop an analytical mindset and prepare them to use data analytic programming languages like Python and R. We’ve divided the course into three main sections. In the first section, we bridge accountancy to analytics. We identify how tasks in the five major subdomains of accounting (i.e., financial, managerial, audit, tax, and systems) have historically required an analytical mindset, and we then explore how those tasks can be completed more effectively and efficiently by using big data analytics. We then present a FACT framework for guiding big data analytics: Frame a question, Assemble data, Calculate the data, and Tell others about the results. In the second section of the course, we emphasize the importance of assembling data. Using financial statement data, we explain desirable characteristics of both data and datasets that will lead to effective calculations and visualizations. In the third, and largest section of the course, we demonstrate and explore how Excel and Tableau can be used to analyze big data. We describe visual perception principles and then apply those principles to create effective visualizations. We then examine fundamental data analytic tools, such as regression, linear programming (using Excel Solver), and clustering in the context of point of sale data and loan data. We conclude by demonstrating the power of data analytic programming languages to assemble, visualize, and analyze data. We introduce Visual Basic for Applications as an example of a programming language, and the Visual Basic Editor as an example of an integrated development environment (IDE)....




It teaches us the basics of data analytics and it is very progressive. There are assignments to help us understand and practice the methods being taught. This allows us to have first-hand experiences.



I have learn a lot of solid knowledge about Data Visualization, Excel VBA, and programming hints from this course. I recommend this course to those who wants to skill up on Excel and Data analytic.


Introduction to Accounting Data Analytics and Visualization: 51 - 71 / 71 レビュー

by Ouyang Y


The course outline is clear and the contents are useful.



It is a nice course. Intructions were delivered clearly

by Gilda H


Great course, great professor, great learning platform.

by Samuel L K L


Is something new and interesting for me. Thank you

by Shawn S W X


Great course, very informative and detailed.

by Nga P R K


Very educational and eye-opening.



useful and good interpretations

by Jim H


Fantastic learning experience.

by Alejandra R


Best class to take at UIUC!

by Saviour U



by Shuvro D B



by Huda A A A A



by Orman A


Lectures were very engaging and informative. Integrated many elements of analysis and thinking that were helpful to learning statistical and and accounting concepts. It would have been helpful to have access to the same data sets as were used by the instructor to follow along with some of the exercises.

I highly recommend this course to anyone wanting to unlock more value from data about their organization or anyone wanting to learn or refresh on statistics and data modeling.

by Aw Z Y


The course teaches a lot of useful data analytics and visualization techniques which will prove to be useful in the future. The only complaint I have is that for the peer review 2, the data provided to us is in csv format. As such, please beware and save your work in xlsx format otherwise, all your progress will be loss if you are currently working on their csv file provided and forgotten to save as xlsx file.

by shuen


Big thanks to Ron and the production team, they did a good job making clear videos, that are easy to follow and break up the topics into well-paced chunks. Covers a nice foundation for data analytics principles, terms, and excel and tableau software. Only thing is it is a bit lengthy, don't try to rush this course.

by Joshua L J W


useful as an intro course to analytics and data visualization. The nature of the content itself is a bit dry but i think the lecturer did not bad and was clear and concise in delivery.

by Kudakwashe C


The course is good but difficult in the sense that computer knowledge must be acquired first.

by John W


Good Course

by Sreelakshmi M


Advanced topics are covered in a quick pace in a basic way

by Stephanie T


i cannot hear a word the professor is saying.

by Deleted A


I just want to unenroll